6  Reshaping and pivots

6.1 Resources

See code and gifs here which illustrate pivots (and indeed other tidyverse verbs).

6.2 Dependencies

Code 
library(dplyr)
library(tidyr)
library(tibble)
#install.packages("devtools")
#devtools::install_github("debruine/faux")
library(faux)
library(janitor)
library(ggplot2)
library(scales)
library(psych)
library(readr)
library(knitr)
library(kableExtra)

6.3 Example

6.3.1 Simulate data in wide format

Code 
# set seed for reproducibility
set.seed(123)

# generate data 
data_wide <- 
  faux::rnorm_multi(n = 100,
                    vars = 5,
                    mu = 3,
                    sd = 1,
                    r = 0.5,
                    varnames = paste0("item_", 1:5),
                    empirical = FALSE) %>%
  rownames_to_column(var = "id")

# recode responses less than 1 or more than 5 to those values, then round scores to whole numbers
# note that {faux} has functions for doing this better


# dat <- data_wide |>
#   mutate(item_1 = round_half_up(item_1, digits = 0),
#          item_1 = ifewlse

data_wide_likert <- data_wide %>%
  mutate(across(starts_with("item_"), ~ round_half_up(.x, digits = 0))) %>%
  mutate(across(starts_with("item_"), ~ ifelse(.x < 1, 1, ifelse(.x > 5, 5, .x))))

6.3.2 Cronbach’s alpha

Wide data like this is a) common and b) useful for calculating metrics like internal consistency.

Code 
res_alpha <- data_wide_likert %>%
  #select(-id) %>%
  select(starts_with("item_")) %>%
  psych::alpha()

cronbachs_alpha_estimate <- res_alpha$total$raw_alpha |>
  round_half_up(digits = 2)

Cronbach’s \(\alpha\) = 0.79

6.3.3 Plot simulated data

Code 
ggplot(data_wide_likert, aes(x = item_1)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw()

Code 
ggplot(data_wide_likert, aes(x = item_2)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw()

Code 
ggplot(data_wide_likert, aes(x = item_3)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw()

Code 
ggplot(data_wide_likert, aes(x = item_4)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw()

Code 
ggplot(data_wide_likert, aes(x = item_5)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw()

  • These plots repeat the mortal coding sin of repeating ourselves. If we reshaped the data to ‘long’ format we could use just one ggplot() call that includes facet_wrap().

6.3.4 Reshape

Using pivot_longer().

Code 
# positive selection
data_long <- data_wide_likert %>%
  pivot_longer(cols = starts_with("item_"),
               names_to = "item",
               values_to = "response")

# positive selection using a different tidy select function
data_long <- data_wide_likert %>%
  pivot_longer(cols = contains("item_"),
               names_to = "item",
               values_to = "response")

# negative selection
data_long <- data_wide_likert %>%
  pivot_longer(cols = -id,
               names_to = "item",
               values_to = "response") |>
  mutate(item = stringr::str_remove(item, "item_"))

ggplot(data_long, aes(x = response)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw() +
  facet_wrap(~ item)

  • What other ways could you specify this pivot_longer call’s arguments?
  • facet_wrap() is to {ggplot} as group_by() is to {dplyr}

6.3.4.1 Calculate sum scores

Code 
temp <- data_wide_likert |>
  group_by(id) |>
  mutate(sum_score = item_1 + item_2 + item_3 + item_4 + item_5)
  #mutate(sum_score = rowSums(item_1, item_2, item_3, item_4, item_5))
  • row math is much faster than column math in R!
Code 
sum_scores <- data_long %>%
  group_by(id) %>%
  summarise(sum_score = sum(response))


ggplot(sum_scores, aes(x = sum_score)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  scale_x_continuous(breaks = breaks_pretty(n = 10)) +
  theme_linedraw()

6.3.5 Convert this long data back to wide

Just to know how to do it.

Code 
data_wide_again <- data_long %>%
  pivot_wider(names_from = item,
              values_from = response,
              names_prefix = "item_")

6.3.6 Combine item and sum scores in one data frame

Code 
data_item_and_sum_scores <- data_wide_again %>%
  left_join(sum_scores, by = "id")

# why joins are needed over bind_cols 
# wrong <- bind_cols(data_wide_again, sum_scores |> select(-id))

6.4 New facet plot with items and sum score

Code 
data_long_with_sum_score <- data_item_and_sum_scores %>%
  pivot_longer(cols = -id,
               names_to = "item",
               values_to = "response")

ggplot(data_long_with_sum_score, aes(x = response)) +
  geom_histogram(binwidth = 1, boundary = -0.5) +
  theme_linedraw() +
  facet_wrap(~ item, scales = "free")

6.5 Practice

Wrangle the demographics data included in this exercise more efficiently by reshaping it into wide format. Before, we used filter() to wrangle the age and gender data separately.

Code 
dat <- read_csv("../data/raw/data_demographics_raw.csv")